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Systematic review of blood transcriptome profiling in neuropsychiatric disorders: guidelines for biomarker discovery

Systematic review of blood transcriptome profiling in neuropsychiatric disorders: guidelines for biomarker discovery
Systematic review of blood transcriptome profiling in neuropsychiatric disorders: guidelines for biomarker discovery
Introduction: The utility of blood for genome-wide gene expression profiling and biomarker discovery has received much attention in patients diagnosed with major neuropsychiatric disorders. While numerous studies have been conducted, statistical rigor and clarity in terms of blood-based biomarker discovery, validation, and testing are needed.

Methods: We conducted a systematic review of the literature to investigate methodological approaches and to assess the value of blood transcriptome profiling in research on mental disorders. We were particularly interested in statistical considerations related to machine learning, gene network analyses, and convergence across different disorders.

Results: A total of 108 peripheral blood transcriptome studies across 15 disorders were surveyed: 25 studies used a variety of machine learning techniques to assess putative clinical viability of the candidate biomarkers; 11 leveraged a higher-order systems-level perspective to identify gene module-based biomarkers; and nine performed analyses across two or more neuropsychiatric phenotypes. Notably, ~50% of the surveyed studies included fewer than 50 samples (cases and controls), while ~75% included less than 100.

Conclusions: Detailed consideration of statistical analysis in the early stages of experimental planning is critical to ensure blood-based biomarker discovery and validation. Statistical guidelines are presented to enhance implementation and reproducibility of machine learning and gene network analyses across independent studies. Future studies capitalizing on larger sample sizes and emerging next-generation technologies set the stage for moving the field forwards.
0885-6222
373-381
Breen, Michael
2a4241cd-4f16-4f7f-9165-1459ed2c8890
Stein, Dan J
81ae9dac-89c4-446a-bda0-73d12749be45
Baldwin, David
1beaa192-0ef1-4914-897a-3a49fc2ed15e
Breen, Michael
2a4241cd-4f16-4f7f-9165-1459ed2c8890
Stein, Dan J
81ae9dac-89c4-446a-bda0-73d12749be45
Baldwin, David
1beaa192-0ef1-4914-897a-3a49fc2ed15e

Breen, Michael, Stein, Dan J and Baldwin, David (2016) Systematic review of blood transcriptome profiling in neuropsychiatric disorders: guidelines for biomarker discovery. Human Psychopharmacology Clinical and Experimental, 31 (5), 373-381. (doi:10.1002/hup.2546).

Record type: Article

Abstract

Introduction: The utility of blood for genome-wide gene expression profiling and biomarker discovery has received much attention in patients diagnosed with major neuropsychiatric disorders. While numerous studies have been conducted, statistical rigor and clarity in terms of blood-based biomarker discovery, validation, and testing are needed.

Methods: We conducted a systematic review of the literature to investigate methodological approaches and to assess the value of blood transcriptome profiling in research on mental disorders. We were particularly interested in statistical considerations related to machine learning, gene network analyses, and convergence across different disorders.

Results: A total of 108 peripheral blood transcriptome studies across 15 disorders were surveyed: 25 studies used a variety of machine learning techniques to assess putative clinical viability of the candidate biomarkers; 11 leveraged a higher-order systems-level perspective to identify gene module-based biomarkers; and nine performed analyses across two or more neuropsychiatric phenotypes. Notably, ~50% of the surveyed studies included fewer than 50 samples (cases and controls), while ~75% included less than 100.

Conclusions: Detailed consideration of statistical analysis in the early stages of experimental planning is critical to ensure blood-based biomarker discovery and validation. Statistical guidelines are presented to enhance implementation and reproducibility of machine learning and gene network analyses across independent studies. Future studies capitalizing on larger sample sizes and emerging next-generation technologies set the stage for moving the field forwards.

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More information

Accepted/In Press date: 15 July 2016
e-pub ahead of print date: 20 September 2016
Published date: September 2016
Organisations: Clinical & Experimental Sciences

Identifiers

Local EPrints ID: 403980
URI: http://eprints.soton.ac.uk/id/eprint/403980
ISSN: 0885-6222
PURE UUID: f1b3b4b3-8b61-4663-9017-320e1e18c16d
ORCID for David Baldwin: ORCID iD orcid.org/0000-0003-3343-0907

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Date deposited: 19 Dec 2016 11:36
Last modified: 16 Mar 2024 02:49

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Contributors

Author: Michael Breen
Author: Dan J Stein
Author: David Baldwin ORCID iD

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